Double Regularization Matrix Factorization Recommendation Algorithm

Du, Ruizhong; Lu, Jiaojiao; Cai, Hongyun

doi:10.1109/access.2019.2943600

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…(i) Input: history rating data of user i, F i,j (ii) Output: R i,j (2) if F i,j <�ratio i (1): (3) then R i,j � 1 (4) else if F i,j <�ratio i (1)+ratio i (2): (5) then R i,j � 2 (6) else if F i,j <�ratio i (1)+ratio i (2)+ratio i (3): (7) then R i,j � 3 (8) else if Matching <�ratio i (1)+ratio i (2)+ratio i (3)+ratio i (4): (9) then R i,j � 4 (10) else: (11) then R i,j � 5 ALGORITHM 1: Predicting R i,j .…”

Section: Evaluation Results and Analysesmentioning

confidence: 99%

“…In recent years, major advances have been made in overcoming the sparsity problem. For example, to improve the performance of matrix factorization recommendation method, which is one of the most popular modern recommendation methods, R. Du et al [7] add user attribute information, Liu et al [8] add product content information, Yulong Gu [9] adds contextual information, He et al [10] and Rong-Ping Shen et al [11] add user feedback information, and Li and Guo [12] add user local characteristics.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Implicit Feedback Recommendation Method Based on User-Generated Content

Fang

Shen

et al. 2021

Scientific Programming

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Studying recommendation method has long been a fundamental area in personalized marketing science. The rating data sparsity problem is the biggest challenge of recommendations. In addition, existing recommendation methods can only identify user preferences rather than customer needs. To solve these two bottleneck problems, we propose a novel implicit feedback recommendation method using user-generated content (UGC). We identify product feature and customer needs from UGC using Convolutional Neural Network (CNN) model and textual semantic analysis techniques, measure user-product fit degree introducing attention mechanism and antonym mechanism, and predict user rating based on user-product fit degree and user history rating data. Using data from a large-scale review sites, we demonstrate the effectiveness of our proposed method. Our study makes several research contributions. First, we propose a novel recommendation method with strong robustness against sparse rating data. Second, we propose a novel recommendation method based on the customer need-product feature fit. Third, we propose a novel approach to measure the fit degree of customer needs-product feature, which can effectively improve the performance of recommendation method. Our study also indicates the following findings: (1) UGC can be used to predict user ratings with no user rating records. This finding has important implications to solve the sparsity problem of recommendations thoroughly. (2) The customer need-based recommendation method has better performance than existing user preference-based recommendation methods. This finding sheds light on the necessity of mining customer need for recommendation methods. (3) UGC can be used to mine customer need and product features. This finding indicates that UGC also can be used in the other studies requiring information about customer need and product feature. (4) Comparing the opinions of user review should not be solely on the basis of semantic similarity. This finding sheds light on the limitation of existing opinion mining studies.

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Section: Evaluation Results and Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Implicit Feedback Recommendation Method Based on User-Generated Content

Fang

Shen

et al. 2021

Scientific Programming

View full text Add to dashboard Cite

show abstract

“…The authors in [25], present a non-negative variant of matrix factorization that integrates social trust information in a model that addresses data sparsity and cold start issues. With a similar trend, the authors in [26] integrate social information into their recommender system based on a matrix factorization method. From implicit feedbacks, the authors in [27] propose a personalized ranking method using a Bayesian pairwise learning to improve the recommendation performances that are based on the matrix factorization technique.…”

Section: B Model-based Methodsmentioning

confidence: 98%

A Bayesian Inference Based Hybrid Recommender System

2020

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The large mass of various products/services accessible on the Internet has motivated the development of recommender systems to refine the selection of items aligned with users' expectations. Recommender systems have been developed to tackle the item targeting problem. They are crucial tools that quickly target items fitting users' needs, thus allowing them to easily identify the items that fit their tastes and preferences. Following state-of-the-art methods, a distinction is made between content-based recommender approaches and collaborative filtering-based recommender approaches. Collaborative filtering-based recommender approaches are the most widely adopted methods. They are divided into memory-based methods that show the advantage of their easy-understandability, and model-based methods that are data sparsity resilient and high-accurate. In this paper, we propose a hybrid model-based recommendation approach, a combination of a user-based approach and an item-based approach. Our method estimates the probability with which a user would rate an item. It performs a Bayesian inference of future end-user interests and shows the advantage of the easy-understandability of memory-based methods and the effectiveness of modelbased methods. Experiments are conducted on real-world datasets and show that our method outperforms several state-of-the-art recommendation methods regarding the prediction accuracy and the recommendation quality.

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A deep neural network-based collaborative filtering using a matrix factorization with a twofold regularization

Ngaffo

Choukair

2022

Neural Comput & Applic

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Double Regularization Matrix Factorization Recommendation Algorithm

Cited by 8 publications

References 15 publications

Implicit Feedback Recommendation Method Based on User-Generated Content

Implicit Feedback Recommendation Method Based on User-Generated Content

A Bayesian Inference Based Hybrid Recommender System

A deep neural network-based collaborative filtering using a matrix factorization with a twofold regularization

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